O intransigente BC e seus juros escorchantes
Valor Econômico, 09/07/2004
Márcio Garcia.
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Valor Econômico, 09/07/2004
Márcio Garcia.
07/07/2004
Fernando Nascimento de Oliveira.
Orientador: Walter Novaes.
Banca: Ilan Goldfajn. Marcelo Fernandes. Márcio Garcia. Ricardo Brito. Walter Novaes.
Jornal "O Estado de São Paulo", 05/07/2004
Marcelo de Paiva Abreu.
TD n. 486, 01/07/2004
In this paper a flexible multiple regime GARCH(1,1)-type model is developed to describe the sign and size asymmetries and intermittent dynamics in financial volatility. The results of the paper are important to other nonlinear GARCH models. The proposed model nests some of the previous specifications found in the literature and has the following advantages. First, contrary to most of the previous models, more than two limiting regimes are possible, and the number of regimes is determined by a simple sequence of tests that circumvents identification problems that are usually found in nonlinear time series models. The second advantage is that the novel stationarity restriction on the parameters is relatively weak, thereby allowing for rich dynamics. It is shown that the model may have explosive regimes but can still be strictly stationary and ergodic. A simulation experiment shows that the proposed model can generate series with high kurtosis and low first-order autocorrelation of the squared observations and exhibit the so-called Taylor effect, even with Gaussian errors. Estimation of the parameters is addressed, and the asymptotic properties of the quasi-maximum likelihood estimator are derived under weak conditions. A Monte-Carlo experiment is designed to evaluate the finite-sample properties of the sequence of tests. Empirical examples are also considered.
Publicado em Econometric Theory, v.25, p. 117-161, 2009
Marcelo Medeiros, Álvaro Veiga.
TD n. 485, 01/07/2004
In this paper, we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR,0), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.
publicado no International Journal of Forecasting, v.21, issue 4, October–December 2005, Pages 755–774
Dick van Dijk, Marcelo Medeiros, Timo Terasvirta.
30/06/2004
Luiza Leite Garcia Fridman.
Orientador: Maria de Nazareth Maciel.
30/06/2004
Maria Izabel Magalhães Gomes Ramos.
Orientador: Nilto Calixto Silva.
30/06/2004
Maria José Baptista Matias.
Orientador: Roberto Magno Iglesias.
30/06/2004
Mariana Pereira Jorge de Barros.
Orientador: Márcio Antônio Scalercio.
30/06/2004
Marianna Schleder.
Orientador: Roberto Magno Iglesias.
30/06/2004
Pedro de Oliveira Valente.
Orientador: Luiz Roberto de Azevedo Cunha.
30/06/2004
Rafael Lagarrigue Dale.
Orientador: Fabrício Mello R. da Silva.
30/06/2004
Rafael Pedra Durães.
Orientador: Sandra Maria Carreira Polônia Rios.
30/06/2004
Raquel Cunha Tessarollo.
Orientador: Gustavo Gonzaga. Mauricio Cortez Reis.
30/06/2004
Renato da Silva Carraro Bastos.
Orientador: José Henrique Tinoco de Araújo.
30/06/2004
Rodolfo Ceppas Bastos.
Orientador: José Henrique Tinoco de Araújo.
30/06/2004
Rodolfo Goytacaz Cavalheiro.
Orientador: Márcio Garcia.
30/06/2004
Lucas Loureiro Maciel de Moura.
Orientador: Sergio Besserman Vianna.
30/06/2004
Lucas Herdy Gondim Murtinho.
Orientador: Rogério Werneck.